On the Road to Speed-Reading and Fast Learning with CONCEPTUM

This work introduces CONCEPTUM, an advanced knowledge discovery system for speed-reading natural language texts and allowing faster and more effective learning. CONCEPTUM sports a huge plethora of features, ranging from language detection and conceptualization, up to semantic categorization, named entity recognition and automatic ontology building, effectively turning an unstructured textual source into concepts, topics, relationships and summaries to quickly and easily browse it and classify it. The system does not require any training or configuration and at present can be applied as-is on general-purpose English and Italian texts, providing disparate kinds of users with a powerful means to significantly speed up and improve their learning and research activities. In this work, a challenging experimentation on the Biochemistry field is reported to highlight and discuss the arising critical issues in the application of the system on a highly-technical domain.

[1]  Fabio Polticelli,et al.  A framework for semi-automatic identification, disambiguation and storage of protein-related abbreviations in scientific literature , 2011, 2011 IEEE 27th International Conference on Data Engineering Workshops.

[2]  Daniele Toti,et al.  A Visual Ontology Management System for Handling, Integrating and Enriching Semantic Repositories , 2015, 2015 International Conference on Intelligent Networking and Collaborative Systems.

[3]  Pierluigi Ritrovato,et al.  ARISTOTELE: A Semantic-Driven Platform for Enterprise Management , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[4]  Fabio Polticelli,et al.  A Knowledge Discovery Methodology for Semantic Categorization of Unstructured Textual Sources , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[5]  Pierluigi Ritrovato,et al.  A Semantic-Based Architecture for Managing Knowledge-Intensive Organizations: The ARISTOTELE Platform , 2011, WISE Workshops.

[6]  Fabio Polticelli,et al.  Experimentation of an automatic resolution method for protein abbreviations in full-text papers , 2011, BCB '11.

[7]  Elizabeth Gibney.  How to tame the flood of literature , 2014, Nature.

[8]  Daniele Toti,et al.  Ontology-driven Data Acquisition: Intelligent Support to Legal ODR Systems , 2013, JURIX.

[9]  Hans-Michael Müller,et al.  Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature , 2004, PLoS biology.

[10]  Daniele Toti,et al.  Semi-automatic Generation of an Object-Oriented API Framework over Semantic Repositories , 2015, 2015 International Conference on Intelligent Networking and Collaborative Systems.

[11]  Fabio Polticelli,et al.  Automatic Protein Abbreviations Discovery and Resolution from Full-Text Scientific Papers: The PRAISED Framework , 2012, Bio Algorithms Med Syst..

[12]  Corie Lok.  Literature mining: Speed reading , 2010, Nature.

[13]  Fabio Polticelli,et al.  An Automatic Identification and Resolution System for Protein-Related Abbreviations in Scientific Papers , 2011, EvoBio.

[14]  Daniele Toti,et al.  Ontology-driven Generation of Training Paths in the Legal Domain , 2015, iJET.

[15]  Pierluigi Ritrovato,et al.  S-WOLF: Semantic Workplace Learning Framework , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[17]  Pierluigi Ritrovato,et al.  Adaptive Feedback Improving Learningful Conversations at Workplace , 2013 .

[18]  Daniele Toti.  AQUEOS: A System for Question Answering over Semantic Data , 2014, 2014 International Conference on Intelligent Networking and Collaborative Systems.

[19]  Matteo Gaeta,et al.  Automatic generation of assessment objects and Remedial Works for MOOCs , 2013, 2013 12th International Conference on Information Technology Based Higher Education and Training (ITHET).

[20]  Daniele Toti,et al.  A Methodology based on Commonsense Knowledge and Ontologies for the Automatic Classification of Legal Cases , 2014, WIMS '14.

[21]  Pierluigi Ritrovato,et al.  ARISTOTELE: An Environment for Managing Knowledge-Intensive Enterprises , 2013, SEBD.

[22]  Fabio Polticelli,et al.  Automatic Discovery and Resolution of Protein Abbreviations from Full-Text Scientific Papers: A Light-Weight Approach Towards Data Extraction from Unstructured Biological Sources (Extended Abstract) , 2011, SEBD.

[23]  Fabio Polticelli,et al.  Knowledge Discovery from Textual Sources by using Semantic Similarity , 2012, SEBD.

[24]  Nicola Capuano,et al.  Ontology Extraction from Existing Educational Content to Improve Personalized e-Learning Experiences , 2009, 2009 IEEE International Conference on Semantic Computing.